Why New Hiring Algorithms Are More Efficient — Even If They Filter Out Qualified Candidates

It doesn't take much effort to apply to a job. Sometimes you
actually have to write a cover letter and send in your resume.
Other times, you just click "apply" on LinkedIn and
— voilà! — your online profile is sent to the hiring
manager.

Ironically, this ease in applying is the reason why the
process is so frustrating for both sides of the job application
process. Qualified jobseekers never hear back and recruiters
don't know how to sift through the hundreds of thousands of
resumes they regularly receive.

"The Internet has
democratized the entire application process," says job site
Bright.com CEO Steve Goodman. "Anybody can go online and spray
and pray their resume all over the place."

That's why it's actually
OK if increasingly complicated algorithms accidentally filter out
some qualified candidates in order to identify the really good
ones, Goodman tells Business Insider.

Launched in early 2011, Bright.com works almost like a
dating site, using data and algorithms to match candidates up
with potential jobs and hiring managers with star performers. The
company currently has 30 million job
descriptions and about 10 million job seekers using the
site.

Similarly to when dating sites "match" up singles based on
common interests, Bright.com offers a "Bright score" to
both jobseekers and employers. This score
incorporates hundreds of
variables, including education, prior employment, and skills
listed to infer other skills that may not be listed. For example,
if you're in public relations, the company assumes that you're
also a good public speaker and speech writer even if you don't
actually list those skills on your resume.

"We take your resume and build a bigger resume around it,"
says Goodman.

The algorithm
also picks up on specific employer's hiring practices to come
up with patterns. For example, the algorithm knows if Citi likes
to hire people who attended Columbia University or worked at
JPMorgan, but typically doesn't hire people from Morgan
Stanley.

"Over time,
the algorithm learns," says Bright.com's
Chief Scientist David Hardtke. In much the same way that
Google learns what their users' interests are over time by
documenting search history, Bright.com analyzes the hiring
patterns of employers to predict the type of candidate that
employer would most want to hire.

With all of
the gathered information, the algorithm then assesses
each candidate's fit for specific available jobs and provides
a numerical
assessment on a scale from zero to
100, with a score of 70 meaning the candidate is minimally
qualified. The database finds the highest score matches and
provides the information to both jobseekers and employers.

Perhaps problematically, this filtering system also eliminates
some qualified candidates. For example, a candidate who never
worked at JPMorgan or went to a non-preferred school might be
given a lower score for a job with Citi even if they would be a
good fit.

"Perfect is the enemy of good," says Hardtke. "It turns out
that, yes, we miss 35% of the good matches, but compared to
what's out there now, it's way, way better. What do companies do
now? Deloitte has turned off all recruiting except for new Ivy
league graduates and internal referrals. The people they look at
is limited to these two groups. That's eliminating way more than
35%."

Hardtke says this filtering system is also better than companies
using keywords to eliminate candidates, which weeds out an
"enormous" group of qualified candidates.

"People do fall through the cracks, there's no question
about it," says Goodman. "But people don't fall through the
cracks with every job. They fall through the cracks with one job
here, one job there."

Goodman says that as long as the algorithm is able to make
the "whole hiring process more efficient," it's OK that a few
qualified candidates were eliminated from jobs they would be a
good fit for.